# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # Copyright 2025 Alibaba Z-Image Team and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect import json import os from collections.abc import Callable, Iterable from typing import Any import torch import torch.nn as nn from diffusers.image_processor import VaeImageProcessor from diffusers.models.autoencoders import AutoencoderKL from diffusers.schedulers import FlowMatchEulerDiscreteScheduler from diffusers.utils import logging from diffusers.utils.torch_utils import randn_tensor from transformers import AutoModel, AutoTokenizer from vllm.model_executor.models.utils import AutoWeightsLoader from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig from vllm_omni.diffusion.distributed.utils import get_local_device from vllm_omni.diffusion.model_loader.diffusers_loader import DiffusersPipelineLoader from vllm_omni.diffusion.models.z_image.z_image_transformer import ( ZImageTransformer2DModel, ) from vllm_omni.diffusion.quantization import get_vllm_quant_config_for_layers from vllm_omni.diffusion.request import OmniDiffusionRequest from vllm_omni.model_executor.model_loader.weight_utils import ( download_weights_from_hf_specific, ) logger = logging.get_logger(__name__) # pylint: disable=invalid-name def get_post_process_func( od_config: OmniDiffusionConfig, ): model_name = od_config.model if os.path.exists(model_name): model_path = model_name else: model_path = download_weights_from_hf_specific(model_name, None, ["*"]) vae_config_path = os.path.join(model_path, "vae/config.json") with open(vae_config_path) as f: vae_config = json.load(f) vae_scale_factor = 2 ** (len(vae_config["block_out_channels"]) - 1) if "block_out_channels" in vae_config else 8 image_processor = VaeImageProcessor(vae_scale_factor=vae_scale_factor * 2) def post_process_func( images: torch.Tensor, ): return image_processor.postprocess(images) return post_process_func # Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift def calculate_shift( image_seq_len, base_seq_len: int = 256, max_seq_len: int = 4096, base_shift: float = 0.5, max_shift: float = 1.15, ): m = (max_shift - base_shift) / (max_seq_len - base_seq_len) b = base_shift - m * base_seq_len mu = image_seq_len * m + b return mu # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps def retrieve_timesteps( scheduler, num_inference_steps: int | None = None, device: str | torch.device | None = None, timesteps: list[int] | None = None, sigmas: list[float] | None = None, **kwargs, ) -> tuple[torch.Tensor, int]: r""" Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. Args: scheduler (`SchedulerMixin`): The scheduler to get timesteps from. num_inference_steps (`int`): The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` must be `None`. device (`str` or `torch.device`, *optional*): The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. timesteps (`list[int]`, *optional*): Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed, `num_inference_steps` and `sigmas` must be `None`. sigmas (`list[float]`, *optional*): Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed, `num_inference_steps` and `timesteps` must be `None`. Returns: `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the second element is the number of inference steps. """ if timesteps is not None and sigmas is not None: raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values") if timesteps is not None: accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) if not accepts_timesteps: raise ValueError( f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" f" timestep schedules. Please check whether you are using the correct scheduler." ) scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) timesteps = scheduler.timesteps num_inference_steps = len(timesteps) elif sigmas is not None: accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) if not accept_sigmas: raise ValueError( f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" f" sigmas schedules. Please check whether you are using the correct scheduler." ) scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs) timesteps = scheduler.timesteps num_inference_steps = len(timesteps) else: scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) timesteps = scheduler.timesteps return timesteps, num_inference_steps class ZImagePipeline(nn.Module): def __init__( self, *, od_config: OmniDiffusionConfig, prefix: str = "", ): super().__init__() self.od_config = od_config self.weights_sources = [ DiffusersPipelineLoader.ComponentSource( model_or_path=od_config.model, subfolder="transformer", revision=None, prefix="transformer.", fall_back_to_pt=True, ) ] self._execution_device = get_local_device() model = od_config.model local_files_only = os.path.exists(model) self.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained( model, subfolder="scheduler", local_files_only=local_files_only ) self.text_encoder = AutoModel.from_pretrained( model, subfolder="text_encoder", local_files_only=local_files_only ) self.vae = AutoencoderKL.from_pretrained(model, subfolder="vae", local_files_only=local_files_only).to( self._execution_device ) # Get vLLM quantization config for linear layers quant_config = get_vllm_quant_config_for_layers(od_config.quantization_config) self.transformer = ZImageTransformer2DModel(quant_config=quant_config) self.tokenizer = AutoTokenizer.from_pretrained(model, subfolder="tokenizer", local_files_only=local_files_only) # Note: Context parallelism is applied centrally in registry.initialize_model() # following diffusers' pattern of enable_parallelism() at model loading time self.vae_scale_factor = ( 2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8 ) def encode_prompt( self, prompt: str | list[str], device: torch.device | None = None, do_classifier_free_guidance: bool = True, negative_prompt: str | list[str] | None = None, prompt_embeds: list[torch.FloatTensor] | None = None, negative_prompt_embeds: torch.FloatTensor | None = None, max_sequence_length: int = 512, ): prompt = [prompt] if isinstance(prompt, str) else prompt prompt_embeds = self._encode_prompt( prompt=prompt, device=device, prompt_embeds=prompt_embeds, max_sequence_length=max_sequence_length, ) if do_classifier_free_guidance: if negative_prompt is None: negative_prompt = ["" for _ in prompt] else: negative_prompt = [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt assert len(prompt) == len(negative_prompt) negative_prompt_embeds = self._encode_prompt( prompt=negative_prompt, device=device, prompt_embeds=negative_prompt_embeds, max_sequence_length=max_sequence_length, ) else: negative_prompt_embeds = [] return prompt_embeds, negative_prompt_embeds def _encode_prompt( self, prompt: str | list[str], device: torch.device | None = None, prompt_embeds: list[torch.FloatTensor] | None = None, max_sequence_length: int = 512, ) -> list[torch.FloatTensor]: device = device or self._execution_device if prompt_embeds is not None: return prompt_embeds if isinstance(prompt, str): prompt = [prompt] for i, prompt_item in enumerate(prompt): messages = [ {"role": "user", "content": prompt_item}, ] prompt_item = self.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True, ) prompt[i] = prompt_item text_inputs = self.tokenizer( prompt, padding="max_length", max_length=max_sequence_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids.to(device) prompt_masks = text_inputs.attention_mask.to(device).bool() prompt_embeds = self.text_encoder( input_ids=text_input_ids, attention_mask=prompt_masks, output_hidden_states=True, ).hidden_states[-2] embeddings_list = [] for i in range(len(prompt_embeds)): embeddings_list.append(prompt_embeds[i][prompt_masks[i]]) return embeddings_list def prepare_latents( self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None, ): height = 2 * (int(height) // (self.vae_scale_factor * 2)) width = 2 * (int(width) // (self.vae_scale_factor * 2)) shape = (batch_size, num_channels_latents, height, width) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: if latents.shape != shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") latents = latents.to(device) return latents @property def guidance_scale(self): return self._guidance_scale @property def do_classifier_free_guidance(self): return self._guidance_scale > 1 @property def joint_attention_kwargs(self): return self._joint_attention_kwargs @property def num_timesteps(self): return self._num_timesteps @property def interrupt(self): return self._interrupt def forward( self, req: OmniDiffusionRequest, prompt: str | list[str] | None = None, height: int = 1024, width: int = 1024, num_inference_steps: int = 50, sigmas: list[float] | None = None, guidance_scale: float = 5.0, cfg_normalization: bool = False, cfg_truncation: float = 1.0, negative_prompt: str | list[str] | None = None, num_images_per_prompt: int = 1, generator: torch.Generator | list[torch.Generator] | None = None, latents: torch.FloatTensor | None = None, prompt_embeds: list[torch.FloatTensor] | None = None, negative_prompt_embeds: list[torch.FloatTensor] | None = None, output_type: str | None = "pil", return_dict: bool = True, joint_attention_kwargs: dict[str, Any] | None = None, callback_on_step_end: Callable[[int, int, dict], None] | None = None, callback_on_step_end_tensor_inputs: list[str] = ["latents"], max_sequence_length: int = 512, ) -> DiffusionOutput: r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `list[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. height (`int`, *optional*, defaults to 1024): The height in pixels of the generated image. width (`int`, *optional*, defaults to 1024): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. sigmas (`list[float]`, *optional*): Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used. guidance_scale (`float`, *optional*, defaults to 5.0): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. cfg_normalization (`bool`, *optional*, defaults to False): Whether to apply configuration normalization. cfg_truncation (`float`, *optional*, defaults to 1.0): The truncation value for configuration. negative_prompt (`str` or `list[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. generator (`torch.Generator` or `list[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will be generated by sampling using the supplied random `generator`. prompt_embeds (`list[torch.FloatTensor]`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`list[torch.FloatTensor]`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.ZImagePipelineOutput`] instead of a plain tuple. joint_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). callback_on_step_end (`Callable`, *optional*): A function that calls at the end of each denoising steps during the inference. The function is called with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by `callback_on_step_end_tensor_inputs`. callback_on_step_end_tensor_inputs (`list`, *optional*): The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the `._callback_tensor_inputs` attribute of your pipeline class. max_sequence_length (`int`, *optional*, defaults to 512): Maximum sequence length to use with the `prompt`. Examples: Returns: [`~pipelines.z_image.ZImagePipelineOutput`] or `tuple`: [`~pipelines.z_image.ZImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images. """ # TODO: In online mode, sometimes it receives [{"negative_prompt": None}, {...}], so cannot use .get("...", "") # TODO: May be some data formatting operations on the API side. Hack for now. prompt = [p if isinstance(p, str) else (p.get("prompt") or "") for p in req.prompts] or prompt if all(isinstance(p, str) or p.get("negative_prompt") is None for p in req.prompts): negative_prompt = None elif req.prompts: negative_prompt = ["" if isinstance(p, str) else (p.get("negative_prompt") or "") for p in req.prompts] height = req.sampling_params.height or height width = req.sampling_params.width or width num_inference_steps = req.sampling_params.num_inference_steps or num_inference_steps generator = req.sampling_params.generator sigmas = req.sampling_params.sigmas or sigmas max_sequence_length = req.sampling_params.max_sequence_length or max_sequence_length guidance_scale = ( req.sampling_params.guidance_scale if req.sampling_params.guidance_rescale is not None else guidance_scale ) num_images_per_prompt = ( req.sampling_params.num_outputs_per_prompt if req.sampling_params.num_outputs_per_prompt > 0 else num_images_per_prompt ) vae_scale = self.vae_scale_factor * 2 if height % vae_scale != 0: raise ValueError( f"Height must be divisible by {vae_scale} (got {height}). " f"Please adjust the height to a multiple of {vae_scale}." ) if width % vae_scale != 0: raise ValueError( f"Width must be divisible by {vae_scale} (got {width}). " f"Please adjust the width to a multiple of {vae_scale}." ) device = self._execution_device self._guidance_scale = guidance_scale self._joint_attention_kwargs = joint_attention_kwargs self._interrupt = False self._cfg_normalization = cfg_normalization self._cfg_truncation = cfg_truncation # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = len(prompt_embeds) # If prompt_embeds is provided and prompt is None, skip encoding if prompt_embeds is not None and prompt is None: if self.do_classifier_free_guidance and negative_prompt_embeds is None: raise ValueError( "When `prompt_embeds` is provided without `prompt`, " "`negative_prompt_embeds` must also be provided for classifier-free guidance." ) else: ( prompt_embeds, negative_prompt_embeds, ) = self.encode_prompt( prompt=prompt, negative_prompt=negative_prompt, do_classifier_free_guidance=self.do_classifier_free_guidance, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, device=device, max_sequence_length=max_sequence_length, ) # 4. Prepare latent variables num_channels_latents = self.transformer.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, torch.float32, device, generator, latents, ) # Repeat prompt_embeds for num_images_per_prompt if num_images_per_prompt > 1: prompt_embeds = [pe for pe in prompt_embeds for _ in range(num_images_per_prompt)] if self.do_classifier_free_guidance and negative_prompt_embeds: negative_prompt_embeds = [npe for npe in negative_prompt_embeds for _ in range(num_images_per_prompt)] actual_batch_size = batch_size * num_images_per_prompt image_seq_len = (latents.shape[2] // 2) * (latents.shape[3] // 2) # 5. Prepare timesteps mu = calculate_shift( image_seq_len, self.scheduler.config.get("base_image_seq_len", 256), self.scheduler.config.get("max_image_seq_len", 4096), self.scheduler.config.get("base_shift", 0.5), self.scheduler.config.get("max_shift", 1.15), ) self.scheduler.sigma_min = 0.0 scheduler_kwargs = {"mu": mu} timesteps, num_inference_steps = retrieve_timesteps( self.scheduler, num_inference_steps, device, sigmas=sigmas, **scheduler_kwargs, ) num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) self._num_timesteps = len(timesteps) # Precompute normalized timesteps once to avoid per-step GPU->CPU sync (.item() causes cudaStreamSynchronize) if isinstance(timesteps, torch.Tensor): timesteps_tensor = timesteps.to(device=device, dtype=torch.float32) else: timesteps_tensor = torch.as_tensor(timesteps, device=device, dtype=torch.float32) norm_timesteps = (1000 - timesteps_tensor) / 1000 t_norm_list = norm_timesteps.cpu().tolist() if not isinstance(t_norm_list, list): t_norm_list = [t_norm_list] # 6. Denoising loop for i, t in enumerate(timesteps): if self.interrupt: continue # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timestep = t.expand(latents.shape[0]) timestep = (1000 - timestep) / 1000 # Normalized time for time-aware config (0 at start, 1 at end); # use precomputed to avoid .item() sync per step t_norm = t_norm_list[i] # Handle cfg truncation current_guidance_scale = self.guidance_scale if ( self.do_classifier_free_guidance and self._cfg_truncation is not None and float(self._cfg_truncation) <= 1 ): if t_norm > self._cfg_truncation: current_guidance_scale = 0.0 # Run CFG only if configured AND scale is non-zero apply_cfg = self.do_classifier_free_guidance and current_guidance_scale > 0 latents_typed = latents.to(self.od_config.dtype) if apply_cfg: latent_model_input = latents_typed.repeat(2, 1, 1, 1) prompt_embeds_model_input = prompt_embeds + negative_prompt_embeds timestep_model_input = timestep.repeat(2) else: latent_model_input = latents_typed prompt_embeds_model_input = prompt_embeds timestep_model_input = timestep latent_model_input = latent_model_input.unsqueeze(2) latent_model_input_list = list(latent_model_input.unbind(dim=0)) model_out_list = self.transformer( latent_model_input_list, timestep_model_input, prompt_embeds_model_input, )[0] if apply_cfg: # Perform CFG pos_out = model_out_list[:actual_batch_size] neg_out = model_out_list[actual_batch_size:] noise_pred = [] for j in range(actual_batch_size): pos = pos_out[j].float() neg = neg_out[j].float() pred = pos + current_guidance_scale * (pos - neg) # Renormalization (torch.where avoids GPU->CPU sync from Python if/scalar comparison) if self._cfg_normalization and float(self._cfg_normalization) > 0.0: ori_pos_norm = torch.linalg.vector_norm(pos) new_pos_norm = torch.linalg.vector_norm(pred) max_new_norm = ori_pos_norm * float(self._cfg_normalization) scale = torch.where( new_pos_norm > max_new_norm, (max_new_norm / new_pos_norm.clamp(min=1e-12)).to(pred.dtype), pred.new_tensor(1.0), ) pred = pred * scale noise_pred.append(pred) noise_pred = torch.stack(noise_pred, dim=0) else: noise_pred = torch.stack([t.float() for t in model_out_list], dim=0) noise_pred = noise_pred.squeeze(2) noise_pred = -noise_pred # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred.to(torch.float32), t, latents, return_dict=False)[0] assert latents.dtype == torch.float32 if callback_on_step_end is not None: callback_kwargs = {} for k in callback_on_step_end_tensor_inputs: callback_kwargs[k] = locals()[k] callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) latents = callback_outputs.pop("latents", latents) prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) if output_type == "latent": image = latents else: latents = latents.to(self.vae.dtype) latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor image = self.vae.decode(latents, return_dict=False)[0] # image = self.image_processor.postprocess(image, output_type=output_type) return DiffusionOutput(output=image) def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: loader = AutoWeightsLoader(self) loaded_weights = loader.load_weights(weights) # Record components loaded by diffusers submodules to satisfy strict checks. loaded_weights |= {f"vae.{name}" for name, _ in self.vae.named_parameters()} loaded_weights |= {f"text_encoder.{name}" for name, _ in self.text_encoder.named_parameters()} return loaded_weights